Survey of curricula: Linguistics and language-related degrees in Europe. UPSKILLS Intellectual output 1.1

open5siThe needs analysis of the UPSKILLS project is the foundation for all subsequent project activities, and the survey of curricula (SoC) as its first step is designed to provide insights for finetuning the interventions and materials that will be designed during the lifetime of the project, as well as for enlarging the pool of stakeholders to whom the project results will be disseminated. The SoC has several steps: drawing a list of European language and linguistics degrees from international ranking websites, selecting and analyzing a representative sample of degrees based on a set of indicators agreed upon by all partners, and additional study of a selection of degrees that the partners identified as exemplary in the context of the UPSKILLS project.openGledić, Jelena; Đukanović, Maja; Miličević Petrović, Maja; van der Lek, Iulianna; Assimakopoulos, StavrosGledić, Jelena; Đukanović, Maja; Miličević Petrović, Maja; van der Lek, Iulianna; Assimakopoulos, Stavro

Graduate skills and employability: Focus interviews with selected job market stakeholders. UPSKILLS Intellectual output 1.5

open9siThe final stage of the UPSKILLS needs analysis involved focus interviews with job market stakeholders. This report presents the method used to conduct and analyse the interviews we carried out with twelve job market stakeholders, the main findings of this UPSKILLS task, a discussion of how these findings relate to the results obtained in the previous steps of the needs analysis, and the aims of the UPSKILLS partnership more generally.openAssimakopoulos, Stavros; Vella, Michela; van der Plas, Lonneke; Milicevic Petrovic, Maja; Samardžić, Tanja; van der Lek, Iulianna; Bernardini, Silvia; Ferraresi, Adriano; Pallottino, MargheritaAssimakopoulos, Stavros; Vella, Michela; van der Plas, Lonneke; Milicevic Petrovic, Maja; Samardžić, Tanja; van der Lek, Iulianna; Bernardini, Silvia; Ferraresi, Adriano; Pallottino, Margherit

Improving the translation environment for professional translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project

Smart Computer-Aided Translation Environment (SCATE): Highlights

We present the highlights of the now finished 4-year SCATE project. It was completed in February 2018 and funded by the Flemish Government IWT-SBO, project No. 130041

How translators work in real-life: SCATE observations

The SCATE (Smart Computer-Assisted Translation Environment) research project is a multidisciplinary co-operation with the objective of improving the translators’ efficiency and consistency through a better integration of existing translation technologies and exploitation of resources. One aspect of this project consisted of undertaking an empirical case study at translators’ workplaces to understand their context of work and how they use translation tools and linguistics resources in real-life settings. During a period of approximately 8 months (November 2014 – September 2015), a total number of 16 translators and terminologists were observed at their workplaces and 4 others were interviewed remotely. The field research took place in Belgium, Luxembourg and the Netherlands, and involved professionals working in different organizational settings: public institutions, language service providers and self-employment. Prior the field observations, we investigated the features of some of the translation environment tools and launched a survey among the translation professionals to get an update on their use of technologies and terminology resources. The results of this empirical study reveal information about the physical working environment, the type of technologies translators use in their daily work and how they use them, and their methods of acquiring domain-specific terminology. In this short presentation we will give an overview of the main findings of the study, with focus on the acquisition of domain knowledge and terminology, and show how SCATE research could tackle some of the problems.status: publishe

Translator's methods of acquiring domain-specific terminology. Information retrieval in terminology using lexical Knowledge Patterns

The SCATE (Smart Computer-Assisted Translation Environment) research project is a multidisciplinary co-operation with the objective of improving translators’ efficiency and consistency through a better integration of existing translation technologies and exploitation of resources. One aspect of this project was to investigate the process of terminology extraction by humans and to automate the process of terminology extraction from comparable corpora. The methods consist of three main tasks: (1) study of translators’ methods to acquire domain knowledge and terminology, (2) determining comparable corpora, and (3) automatic terminology extraction from comparable text. In this presentation we will focus on the results of task (1) which has been completed, and present the preliminary results of task (3). In task (1) we collected the data through an international survey and observations of 16 translators and terminologists at their workplaces across a period of 8 months in 2015. The study revealed information about translators’ web search behaviour and usage of online linguistic resources to solve terminological problems. Besides these, we have identified needs and shortcomings of different CAT tools regarding the terminology management component, integration with online databases and exchange of terminological data. In task (3) we identified two subtasks: monolingual term extraction and term linking (i.e., linking terms to their corresponding translation). For the term extraction, we applied a hybrid approach combining linguistic and statistical information (Macken et al., 2013). Subsequently, two different techniques were investigated to link the translation equivalents: probabilistic topic models as well as different neural network architectures. The best results were obtained with a neural network model. In order to evaluate the performance of the different modules, we created a gold standard for three different domains (heart failure, wind energy, corruption) in three different languages (English, French, Dutch). References Poly-GrETEL. Available online at https://clarin.eu/showcase/poly-gretel-search-engine-querying-syntactic-constructions-parallel-treebanks Macken, L., Lefever, E. & Hoste, V. (2013). TExSIS: Bilingual Terminology Extraction from Parallel Corpora Using Chunk-based Alignment. Terminology, 19 (1), 1-30. John Benjamins Publishing Company, Amsterdam, Netherlands. van den Bergh, Jan et al. (2015). Recommendations for Translation Environments to Improve Translators' workflows. Translating and the Computer 37. Asling. London. van der Lek-Ciudin, Iulianna, Tom Vanallemeersch and Ken de Wachter (2015). Contextual Inquiries at translators’ workplaces. In Proceedings of the 1st TAO-CAT, Angers 2015 TermWise: Resources for Specialised Language Use. Information available online at http://liir.cs.kuleuven.be/projects.php?project=177status: accepte

Assessing interpersonal skills in translator training: the cases of INSTB

International audienc